基于机器学习的预测,地铁护道建设引发的地面表面结算
Qiankun Wang1, Chuxiong Shen2, Chao Tang3
1School of Civil Engineering and Architecture, Wuhan University of Technology, Wuhan, 430070, China.
Scientific reports
|December 31, 2024
概括
城市铁路运输的屏蔽道导致地面定居点,危及附近的结构. 这项研究使用了一种新的数据预处理技术和粒子优化优化的反向传播神经网络 (PSO-BP) 来准确预测定居点,帮助安全评估.
科学领域:
- 地质技术工程 地质技术工程
- 土木工程 土木工程是指土木工程.
- 城市规划 城市规划
背景情况:
- 城市轨道交通系统,就像地铁一样,对于管理城市化和交通拥堵至关重要.
- 盾牌道,一个常见的地铁施工方法,导致地面表面结算,对邻近的建筑构成风险.
- 准确地预测地面沉积物对于屏蔽道挖掘期间的安全警告和评估至关重要.
研究的目的:
- 开发一种可靠的方法来预测由盾牌道引发的地面表面沉积.
- 通过数据预处理和优化神经网络的结合,提高定居点预测的准确性.
- 为安全管理和地面结算的控制提供一个有价值的参考.
主要方法:
- 从监测部分收集的多点地面表面结算数据.
- 应用了基于触角圆的数据预处理方法,将离散数据转换为连续的,光滑的数据.
- 利用粒子优化 (PSO) 算法优化一个反向传播神经网络 (BPNN) 进行定居预测.
- 研究了网络结构和预测模式对预测准确性的影响.
主要成果:
- 拟议的数据预处理和PSO-BP算法在结算预测中实现了0.46%的最大相对误差.
- 该方法表现出良好的匹配效果,在缓慢和稳定的结算阶段在5天内准确预测结算.
- 该研究验证了综合方法的预测性能和有效性.
结论:
- 开发的方法提供了准确和可靠的预测,由盾牌道造成的地面表面结算.
- 这种方法为实时安全警告和地铁施工期间有效控制地面结算提供了有价值的工具.
- 这些发现有助于提高城市地下基础设施开发的安全性和效率.
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